Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training
Hexiao Lu, Xiaokun Sun, Zeyu Cai, Hao Guo, Ying Tai, Jian Yang, Zhenyu Zhang
摘要
We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware optimization, manual assembly, or 2D image generation, often produce unrealistic or incoherent 3D assets due to the challenges of intricate part-level manipulation and limited out-of-domain generation. In contrast, Muses leverages the 3D skeleton, a fundamental representation of biological forms, to explicitly and rationally compose diverse elements. This skeletal foundation formalizes 3D content creation as a structure-aware pipeline of design, composition, and generation. Muses begins by constructing a creatively composed 3D skeleton with coherent layout and scale through graph-constrained reasoning. This skeleton then guides a voxel-based assembly process within a structured latent space, integrating regions from different objects. Finally, image-guided appearance modeling under skeletal conditions is applied to generate a style-consistent and harmonious texture for the assembled shape. Extensive experiments establish Muses'state-of-the-art performance in terms of visual fidelity and alignment with textual descriptions, and potential on flexible 3D object editing. Project page: https://luhexiao.github.io/Muses.github.io/.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper44
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt 等NeurIPS 2021 · 被引用 2,500 次
- ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score DistillationZhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao 等NeurIPS 2023 · 被引用 1,498 次
相关 Paper
- Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion modelsRuisi Zhao, Haoren Zheng, Zongxin Yang, Hehe Fan 等ICLR 2026 · 被引用 2 次
- SKDream: Controllable Multi-view and 3D Generation with Arbitrary SkeletonsYuanyou Xu, Zongxin Yang, Yi YangCVPR 2025
- Interp3D: Correspondence-aware Interpolation for Generative Textured 3D MorphingXiaolu Liu, Yicong Li, Qiyuan He, Jiayin Zhu 等ICLR 2026 · 被引用 6 次
- Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel FlowShimin Hu, Yuanyi Wei, Fei Zha, Yudong Guo 等CVPR 2026 · 被引用 7 次
- PoseMaster: A Unified 3D Native Framework for Stylized Pose GenerationHongyu Yan, Kunming Luo, Weiyu Li, Kaiyi Zhang 等CVPR 2026 · 被引用 1 次
